sequence preprocessing: a perspective · preprocessing •map reads to contaminants/phixand extract...

Post on 05-Oct-2020

2 Views

Category:

Documents

0 Downloads

Preview:

Click to see full reader

TRANSCRIPT

Sequence Preprocessing: A perspective

Dr. Matthew L. Settles

Genome CenterUniversity of California, Davis

settles@ucdavis.edu

WhyPreprocessreads

• Wehavefoundthataggressively“cleaning”andprocessingreadscanmakealargedifferencetothespeed andquality ofassemblyandmappingresults.Cleaningyourreadsmeans,removingreads/basesthatare:• otherunwantedsequence(polyA tailsinRNA-seq data)• artificiallyaddedontosequenceofprimaryinterest(vectors,adapters,primers)

• joinshortoverlappingpaired-endreads• lowqualitybases• originatefromPCRduplication• notofprimaryinterest(contamination)

• Preprocessingalsoproducesanumberofstatisticsthataretechnicalinnaturethatshouldbeusedtoevaluate“experimentalconsistancy”

ReadPreprocessingstrategies,manyovertime

• Identityandremovecontaminantandvectorreads• Readswhichappeartofullycomefromextraneoussequenceshouldberemoved.

• Qualitytrim/cut• “end”trimareaduntiltheaveragequality>Q(Lucy)• removeanyreadwithaveragequality<Q

• eliminatesingletons/duplicates• Ifyouhaveexcessdepthofcoverage,andparticularlyifyouhaveatleastx-foldcoveragewherexisthereadlength,theneliminatingsingletonsisanicewayofdramaticallyreducingthenumberoferror-pronereads.

• Readwhichappearthesame(particularlypaired-end)areoftenmorelikelyPCRduplicatesandthereforredundantreads.

• eliminateallreads(pairs)containingan“N”character• Ifyoucanaffordthelossofcoverage,youmightthrowawayallreadscontainingNs.

• Identityandtrimoffadapterandbarcodesifpresent• Believeitornot,thesoftwareprovidedbyIllumina,eitherdoesnotlookfor,ordoesamediocrejobof,identifyingadaptersandremovingthem.

RibosomalRNA

• RibosomalRNAmakesup90%ormoreofatypicaltotalRNAsample.• LibraryprepmethodsreducetherRNA representationinasample

• oligoDt onlybindstopolyA tailstoenrichasampleformRNA• Ribo-depletionbindsrRNA sequences

Neithertechniqueis100%efficient

Canscreen(mapreadstorRNA sequences)todeterminerRNAefficiencyandpotentiallyremovethosereads.

DNA/RNA,couldcontain‘contamination’Libraryprep,fragmentation,adapteraddition

PCRenrichment

FinalLibrary,sizedistribution PossibleadditionofphiX SequencingCharacteristics/Quality

Preprocessing• Mapreadstocontaminants/PhiX andextractunmappedreads[bowtie2--local

• Removecontaminants(atleastPhiX),usesbowtie2thenextractsallreads(pairs)thataremarkedasunmapped.

• Super-Deduper [PEreadsonly]• RemovePCRduplicates(weusebases10-35ofeachpairedread)

• FLASH2[ PEreadsonly]• Joinandextend,overlappingpairedendreads• Ifreadscompletelyoverlaptheywillcontainadapter,removeadapters• Identifyandremoveanyadapterdimerspresent

• Scythe[SEReadsonly]• Identifyandremoveadaptersequence

• Sickle• Trimsequences(5’and3’)byqualityscore(IlikeQ20)

• cleanup• RunapolyA/Ttrimmer• Removeanyreadsthatarelessthentheminimumlengthparameter• Producepreprocessingstatistics

WhyScreenforPhiX

• PhiX isacommoncontrolinIlluminaruns,facilitiesrarelytellyouif/whenPhiX hasbeenspikedin

• Doesnothaveabarcode,sointheoryshouldnotbeinyourdata

• However• WhenIknowPhiX hasbeenspikedin,Ifindsequenceeverytime• WhenIknowPhiX hasnotbeenspikedin,Idonot findsequence

• Bettersafethansorryandscreenforit.

SuperDeduper

https://github.com/dstreett/Super-Deduper

Read1

Read2

Data AlignmentAlgorithm

MarkDuplicates Rmdup SuperDeduper FastUniq Fulcrum Total#ofReads

PhiX BWAMEM 1,048,278(0.25%)

1,011,145(1.05%)

1,156,700(13.7%)

4,202,526 3,092,155 4,750,299

Bowtie2Local 1,054,725(6.62%)

948,784(10.2%)

1,166,936(14.0%)

4,236,647 3,103,872 4,790,972

Bowtie2Global 799,524(0%)

800,868(0.12%)

896,487(9.92%)

3,768,641 2,704,114 4,293,787

Acroporadigitifera

BWAMEM 5,132,111(2.26%)

6,906,634(44.5%)

5,133,339(10.2%)

12,968,469 2,103,567 54,108,240

Bowtie2Local 4,688,809(4.03%)

5,931,862(38.9%)

3,971,743(9.32%)

9,893,903 4,259,619 41,728,154

Bowtie2Global 1,457,865(3.62%)

1,512,966(24.2%)

1,185,838(11.4%)

3,014,498 1,286,031 11,600,847

SuperDeduper

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00False Positive Rate

True

Pos

itive

Rat

e

10

0.875

0.900

0.925

0.950

0.975

0.03 0.04 0.05 0.06 0.07False Positive Rate

StartPosition

1

5

20

40

150

Figure 1: ROC curves. Only a representative subset of the different start positions is shown. The image on the left shows the full ROC curves and the image on the left is a zoomed in view of corner of the curves. Each curve represents a start position and each point represents a length. The labeled point in the image on the right is the default start and length for

Super Deduper.

We calculated the Youden Index for every combination tested and the point that acquired the highest index value (as compared to Picard MarkDuplicates) occurred at a start position of 5bp and a length of 10bps (20bp total over both reads)

Flash2– overlappingofreadsandadapterremovalinpairedendreads

TargetRegion

Read1

InsertsizeRead2

TargetRegion

Read1

InsertsizeRead2

TargetRegion

Read1

InsertsizeRead2

Insertsize>lengthofthenumberofcycles

Insertsize<lengthofthenumberofcycles(10bpmin)

Insertsize<lengthofthereadlength

Product:ReadPair

Product:Extended,Single

Product:AdapterTrimmed,Single

https://github.com/dstreett/FLASH2

QualityTrimming- Sickle

Remove“poor”qualitysequencefromboththe5’and3’ends

QA/QC

• Beyondgenerating‘better’datafordownstreamanalysis,cleaningstatisticsalsogiveyouanideaastothequalityofthesample,librarygeneration,andsequencingqualityusedtogeneratethedata.

• Thiscanhelpinformyouofwhatyoumightdointhefuture.• I’vefounditbesttoperformQA/QConboththerunasawhole(poorsamplescanaffectothersamples)andonthesamplesthemselvesastheycomparetoothersamples (REMEMBER,BECONSISTANT).

• ReportssuchasBasespace forIllumina,aregreatwaystoevaluatetherunsasawhole.

• PCA/MDSplotsofthepreprocessingsummaryareagreatwaytolookfortechnicalbiasacrossyourexperiment

ComparingMappingRawvsPreprocessedwithstar

top related